Master Thesis - Department of Computer Science

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[14] Y. Bing, J. Lianfu, and C. Ping. A new LDA-based Method for Face Recog- nition. In Proc. Int. Conf. on Pattern Recognition, volume 1, pages 168–171, Aug. 2002. [15] C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press Inc., New York, 1995. [16] R. Brunelli and T. Poggio. Face Recognition: Features versus Templates. IEEE Tran. on Pattern Analysis and Machine Intelligence, 15(10):1042–1052, October 1993. [17] Federal Bureau of Investigation. The Science of Fingerprints: Classification and Uses. U.S. Government Printing Office, Washington, D. C., 1984. [18] B.Yegnanarayana. Artificial Neural Networks. Prentice-Hall of India, New Delhi, 1999. [19] S. Cagnoni and A. Poggi. A Modified Modular Eigenspace Approach to Face Recognition. In 10th International Conference on Image Analysis and Process- ing (ICIAP99), pages 490–495, Venice, Italy, September 1999. [20] Hakan Cevikalp, Marian Neamtu, Mitch Wilkes, and Atalay Barkana. Discrimi- native Common Vectors for Face Recognition. IEEE Tran. on Pattern Analysis and Machine Intelligence, 27(1):4–13, Jan. 2005. [21] V. Chatzis, A. G. Bors, and I. Pitas. Multimodal Decision Level Fusion for Per- son Authentication. IEEE Transactions on System and Man and Cybernetics, 29(6):674–681, November 1999. [22] Vassilios Chatzis, Adrian G. Bors, and Ioannis Pitas. Multimodal Decision- Level Fusion for Person Authentication. IEEE Transactions on System and Man and Cybernetics, 29(6):674–680, November 1999. [23] R. Chellappa, C. L. Wilson, and S. Sirohey. Human and Machine Recognition of Faces: A Survey. Proc. of the IEEE, 83(5):705–740, May 1995. [24] K. Chen, L. Wang, and H. Chi. Methods of Combining Multiple Classifiers with Different Features and Their Application to Test-Independent Speaker Identifi- cation. International Journal of Pattern Recognition and Artificial Intelligence, 11(3):417–445, 1997. 144
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DEVELOPMENT OF EFFICIENT METHODS FO
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To My Parents, Sisters and Dearest
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Mirnalinee, Shreyasee, Lalit, Manis
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LDC Linear Discriminant Classifier
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3 An Efficient Method of Face Recog
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A.6 Minutiae Matching . . . . . . .
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4.1 Effect of increasing number of
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List of Figures 2.1 Summary of appr
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3.7 Area difference between genuine
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Abstract Biometrics is a rapidly ev
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CHAPTER 1 Introduction The issues a
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1.1.1 Applications Biometrics has b
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• Spoof Attacks: An impostor may
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• A new approach for multimodal b
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CHAPTER 2 Literature Review This re
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ange [23], infrared scanned [137] a
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u 2 x 2 u 1 x 1 (a) PCA basis (b) P
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PCA Projection Class 1 Class 2 LDA
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F DIFS DFFS F (a) (b) F 1 L Figure
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image A of m rows and n columns is
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faces of 40 subjects. • Others: A
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malizing the vector components, the
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Figure 2.5: A fingerprint image wit
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features (gradient coherence, inten
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Figure 2.7: Examples of minutiae; A
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according to the local orientation
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• Parallel Mode: This operational
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can address the problem of noisy se
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Prior to Matching Sensor Level Feat
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determined by logistic regression.
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Class-indifferent Methods • Decis
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3. The final DS soft vector is calc
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on three face databases and compare
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for a face. This was illustrated by
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Image Rows h(.) g(.) 2 2 Columns Fi
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in the accuracy of face recognition
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(a) (a1) (a2) (a3) (b) (b1) (b2) (b
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Genuine Impostor Scores Scores 1 0
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Error d1 = (1 − TZeroF RR) d2 = |
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The algorithm for obtaining the sub
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each subject, 42 samples (flashes f
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Table 3.3: Peak Recognition Accurac
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Table 3.5: Peak Recognition Accurac
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Table 3.7: Performance of Ekenel’
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Table 3.9: Performance of Ekenel’
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to our proposed method. Section 4.2
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plored both of the spaces to captur
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project only the class means in ran
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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iii) Project all class means onto n
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Page 115 and 116: Table 4.3: Effect of increasing num
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Page 119 and 120: Table 4.6: Performance of Dual Spac
Page 121 and 122: 5.1 Introduction In recent years, t
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Page 125 and 126: validation1, validation2 and test.
Page 127 and 128: 5.3.1 The Algorithm The steps of ou
Page 129 and 130: 5.4 Experimental Results In this ch
Page 131 and 132: sets. • We put as much as possibl
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Page 135 and 136: LDA and LDA produce the same accura
Page 137 and 138: space. If the training data uniform
Page 139 and 140: face for each subject, and compare
Page 141 and 142: also be introduced. But class speci
Page 143 and 144: These stages are discussed in the f
Page 145 and 146: (a) (b) Figure A.2: Input fingerpri
Page 147 and 148: (a) (b) Figure A.4: Orientation ima
Page 149 and 150: (i) For each block centered at (i,
Page 151 and 152: (a) (b) Figure A.7: Enhanced images
Page 153 and 154: (a) (b) Figure A.10: (a) and (b) sh
Page 155 and 156: (a) (b) Figure A.12: Binarized imag
Page 157 and 158: (a) (b) Figure A.14: Thinned images
Page 159 and 160: (a) (b) Figure A.16: Thinned finger
Page 161 and 162: Paired Minutiae Minutiae with unmat
Page 163: Bibliography [1] FVC 2002 website.
Page 167 and 168: [49] Lin Hong, Yifei Wan, and Anil
Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
Page 171 and 172: IEEE Tran. on Pattern Analysis and
Page 173 and 174: of-the-Art Systems. IEEE Tran. on P
Page 175 and 176: and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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